Aside from a robust knowledge in anatomy and physiology or radiation physics, there’s not much I can use my background in nuclear medicine technology in the practice of law. Which is why in 2009 I noted here the growing and emerging use of diagnostic imaging in sentencing and trials.

Since that time there has been quite a bit of developments in diagnostic imaging and its use in medico-legal work. One of the newest developments is its use for chronic pain. The economic costs of chronic pain are estimated to be over $600 billion in the U.S. Part of the challenge to the legal system is that chronic pain is often perceived to be a subjective experience that cannot be objectively determined by independent third parties.

Chronic pain is costly not only to the patient but also to society as a whole. Expenditures on chronic pain include both direct costs related to treatment and provision of health care services, and indirect costs such as those associated with loss of productivity, lost tax revenues and disability payments. Uncontrolled pain continues to be the single most common cause of disability among working-age adults in Canada. Sixty per cent of people with chronic pain eventually lose their job, incur loss of income or will have a reduction in responsibilities as a result of their pain. For people who are still employed, it is anticipated that they will have a mean of 28.5 lost work days per year.

Chronic pain costs more than cancer, heart disease and HIV combined. Estimates place direct health care costs for Canada at more than $6 billion per year, and productivity costs related to job loss and sick days at $37 billion per year. Patients are referred to pain clinics when first-line treatments are not available, or when severe unremitting pain and/or complex psychosocial situations exist. The economic burden of patients who wait for access to pain clinics was studied by the Canadian STOP-PAIN Research Group. They reported that patients waiting to access pain clinics spent a median of $17,544 per year, the vast majority of which were indirect expenditures, eg, lost labour time and funding of private health care treatments. This demonstrates the significant financial burden that pain has on the individual as well as our society

A new consensus statement, released this month in Nature Reviews, explores the use of diagnostic imaging in chronic pain, and states,

Brain imaging technologies, including functional MRI (fMRI), PET, EEG and magnetoencephalography (MEG), have the potential to provide objective measurements of patterns of brain activity that underlie perceptual experiences (BOX 1). Consequently, some people are looking to brain imaging to provide a window into the experience of chronic pain, particularly because testimony based on fMRI was deemed to be admissible as evidence of pain in a 2015 state trial court in the USA. This case was highly publicized, although the judgement was not published so no legal precedent was set, and the grounds on which the fMRI evidence was admitted were criticized by established experts in brain imaging studies of pain.

By applying machine learning techniques to brain image data, the authors were able to develop a sensitive model to measure differences in brain activity to identify unique pain signatures. They propose 7 criteria for a brain measure appropriate for various clinical and legal applications as follows:

A precise definition of a pain neuromarker

Applicability of the pain neuromarker to individuals

Methodological procedures used during testing must be validated

Measures must be internally consistent and image data quality validated for the individual tested using positive and negative controls

The neuromarker must be diagnostic for pain

The neuromarker must be validated with converging methods

The neuromarker must be generalizable to the patient group tested and to the test conditions

The application for litigation cases would be to educate the trier of fact about chronic pain pathophysiology when assessing evidence, and to properly weigh any diagnostic imaging used as evidence by minimizing the prejudicial impact of highly persuasive but lowly probative imaging. Although brain imaging is still insufficiently reliable to be used as a pain detector to provide objective evidence of the presence or intensity of chronic pain in a litigant, but this continues to be one of the most promising applications of brain imaging for chronic pain cases in the future.

The authors also raise particular ethical questions about pain imaging data, such as employers or insurers who could deny employment or coverage on the basis of being particularly predisposed to chronic pain. The parallels to geneticdiscrimination here are obvious, and would raise similar privacy concerns.

Brain imaging could be used to better obtain informed consent by providing more accurate estimates of chronic pain adverse effects post surgery.

The greater ethical concern could arise where there is increasing pressure to use brain scans for medico-legal purposes before they are properly validated scientifically ,

Today, the necessary scientific knowledge — including the specificity and sensitivity of such tests — and validated protocols to enable use of brain imaging evidence in the legal system do not exist. Until they do, use of brain-based measurements that do not meet minimum standards would be detrimental to health care and legal systems, potentially harmful to patients and claimants, and legally inappropriate (and consequently unethical). In our view, current brain-based measures fall short of the requisite standards for legal proceedings, but we do encourage their use for understanding brain mechanisms that underlie pain, factors that lead to persistence of pain, and targets in the brain for safe and effective pain control.

Comments

The primary use of pain imaging from a legal perspective would be for..? For disability insurance claims or?

Certainly technology has some value in discussion between physician (and relevant health care professional with direct therapeutic methods/measures) on pain management and reduction therapies. As usual there’s a time lag between technology application, results interpretation and training the right health care professional to interpret results.